A stackelberg game man-machine sharing system splicing control method
By employing a Stackelberg game-based approach to splicing control in human-machine shared driving fleet systems, we have addressed the shortcomings of existing systems in terms of control handover and interference interaction. This approach achieves smooth transfer of control and improved robustness, while reducing control costs.
Patent Information
- Application Number
- CN202610799797.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-04
- Publication Date
- 2026-08-25
AI Technical Summary
Existing human-machine shared driving fleet systems have shortcomings in control methods, such as control switching mechanisms. They are unable to achieve a smooth transfer of control and accurately describe the interaction between humans, machines, and interference, and they lack strong robustness.
A Stackelberg game-based splicing control method for human-machine shared systems is adopted. By designing splicing control inputs containing a Sigmoid function, the external disturbance is represented as a three-person Stackelberg differential game. By solving the parameter-related algebraic Riccati equations, the suboptimal human-machine control strategy and the worst-case disturbance are obtained. The information interaction is represented by an undirected topological graph to optimize the allocation of control rights.
It effectively reduces the control cost of the human-machine shared driving fleet system, realizes the smooth transfer of control and accurately describes the human-machine interference interaction relationship, and improves the robustness of the system.
Smart Images
Figure CN122632612A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to an optimization control method, specifically to a stitching control method for a human-machine shared system in Stackelberg game theory. Background Technology
[0002] With the rapid development of autonomous driving technology, human-machine shared driving, as a crucial transitional stage before achieving fully autonomous driving, has become a research hotspot in the field of intelligent transportation. A fleet system composed of multiple human-machine shared driving vehicles can achieve coordinated driving through information interaction between vehicles, significantly improving road efficiency, reducing energy consumption, and decreasing traffic accidents. However, existing control methods for human-machine shared driving fleet systems still have many shortcomings in areas such as control handover mechanisms. Therefore, developing a human-machine shared driving fleet control method that can achieve smooth transfer of control, accurately describe the interaction between humans / machines and disturbances, and possesses strong robustness has become a key problem urgently needing to be solved in this field. Summary of the Invention
[0003] The purpose of this invention is to propose a Stackelberg game-based splicing control method for human-machine shared driving systems, which can effectively reduce the control cost of human-machine shared driving fleet systems.
[0004] The specific technical solution of the present invention is as follows: A method for splicing and controlling a human-computer shared system for Stackelberg game, comprising the following steps:
[0005] Design a concatenated control input with a Sigmoid function to eliminate jitter caused by discontinuous control switching.
[0006] The human-machine shared control under external disturbances is expressed as a three-person Stackelberg differential game. By solving the parameter-dependent algebraic Riccati equations, the suboptimal human-machine control strategy and the worst-case disturbance are obtained.
[0007] Under external disturbances, the human-machine shared driving system behaves as follows:
[0008]
[0009]
[0010]
[0011] In the formula Indicates the number of vehicles. Indicates the first The time constant of a vehicle's power transmission. Indicates the first The quality of each vehicle Indicates the first The location status of each vehicle. Indicates the first The speed status of each vehicle Indicates the first The acceleration state of each vehicle Indicates the first External disturbances to the vehicle. Indicates the first Control inputs for each vehicle;
[0012] Select a new state variable for And using an undirected topological graph to represent the information interaction between the human-machine shared driving fleet systems, the state-space equation of the human-machine shared driving fleet system can be obtained as follows:
[0013] in Indicates matrix transpose. Represents positive integers. and Represents the parameter matrix, Represents the external coupling matrix. Represents the inner coupling matrix. Indicates the first The location status of each vehicle;
[0014] With the help of the Kronecker integrator The system can be further written as
[0015] In the formula State variables In matrix form, External coupling matrix In matrix form, To control input In matrix form, Unknown system failure In matrix form, for An identity matrix of order 1;
[0016] make For a leader to be in a state that satisfies The synchronization error is defined as follows: Obtain the error system
[0017] Based on external disturbances norm Control input Divided into
[0018] in For machine control input, For human control input, The preset disturbance threshold;
[0019] Human-machine permission allocation coefficient Designed for
[0020] in To control the smoothness coefficient during the switching transition phase, It is the Sigmoid function;
[0021] The concatenation control input containing the Sigmoid function is
[0022] To evaluate system performance and ensure interference attenuation, splicing control inputs were used. Corresponding cost function Defined as
[0023] in Synchronization error The corresponding weight matrix, For machine control The corresponding weight matrix, For machine control The corresponding weight matrix, Preset Disturbance attenuation index; will have external interference Human-machine shared control is described as a three-person Stackelberg game, where perturbations Acting as a leader to maximize the cost function, machine control and machine control Act as a follower to minimize the cost function;
[0024] Cost function The corresponding Hamiltonian function is
[0025] in Representing the cost function Regarding synchronization error The gradient;
[0026] According to the minimax principle, the saddle point solution... satisfy
[0027]
[0028]
[0029]
[0030] Solving the above equation yields the suboptimal control input. and maximum disturbance for
[0031]
[0032]
[0033]
[0034] in Is the positive definite solution to the following algebraic Riccati equation?
[0035] In the formula ;
[0036] Suboptimal control input Substituting the concatenated control input containing the Sigmoid function, we can obtain . Attached Figure Description
[0037] Figure 1 The positional trajectory of the follower;
[0038] Figure 2 The velocity trajectory of the follower;
[0039] Figure 3 The trajectory of the follower's acceleration state;
[0040] Figure 4 Human-machine permission allocation coefficient The trajectory;
[0041] Figure 5 For external disturbances The trajectory;
[0042] Figures 6-8 The control input trajectory for the follower;
[0043] Figure 9 The cost function trajectory; Detailed Implementation
[0044] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.
[0045] A method for splicing and controlling a human-machine shared system using Stackelberg game theory includes the following steps:
[0046] Step 1: Set the various system parameters;
[0047] Step 2: Design a splicing control input containing a Sigmoid function to eliminate the jitter caused by discontinuous switching control;
[0048] Step 3: Formulate the human-machine shared control under external disturbances as a three-person Stackelberg differential game, and obtain the suboptimal human-machine control strategy and the worst-case disturbance by solving the parameter-related algebraic Riccati equations.
[0049] An embodiment of the present invention is described below:
[0050] Consider a Stackelberg game-based human-machine shared system splicing control method, with the corresponding dynamic models as follows:
[0051]
[0052]
[0053]
[0054] The positional trajectory of the follower is Figure 1 As shown, the velocity trajectory of the follower is as follows: Figure 2 As shown, the acceleration trajectory of the follower is as follows: Figure 3 As shown, the human-machine permission allocation coefficient The trajectory is as follows Figure 4 As shown, external disturbance The trajectory is as follows Figure 5 As shown, the control input trajectory of the follower is as follows: Figures 6-8 As shown, the cost function trajectory is as follows Figure 9 As shown.
Claims
1. A method for splicing and controlling a human-machine shared system using Stackelberg game theory, characterized in that, Includes the following steps: The design incorporates a concatenated control input with a Sigmoid function to eliminate jitter caused by discontinuous control switching. The specific steps are as follows: Under external disturbances, the human-machine shared driving system behaves as follows: In the formula Indicates the number of vehicles. Indicates the first The time constant of a vehicle's power transmission. Indicates the first The quality of each vehicle Indicates the first The location status of each vehicle. Indicates the first The speed status of each vehicle Indicates the first The acceleration state of each vehicle Indicates the first External disturbances to the vehicle. Indicates the first Control inputs for each vehicle; Select a new state variable for And using an undirected topological graph to represent the information interaction between the human-machine shared driving fleet systems, the state-space equation of the human-machine shared driving fleet system can be obtained as follows: in Indicates matrix transpose. Represents positive integers. and Represents the parameter matrix, Represents the external coupling matrix. Represents the inner coupling matrix. Indicates the first The location status of each vehicle; With the help of the Kronecker integrator The system can be further written as In the formula State variables In matrix form, External coupling matrix In matrix form, To control input In matrix form, Unknown system failure In matrix form, for An identity matrix of order 1; make For a leader to be in a state that satisfies The synchronization error is defined as follows: Obtain the error system Based on external disturbances norm Control input Divided into in For machine control input, For human control input, The preset disturbance threshold; Human-machine permission allocation coefficient Designed for in To control the smoothness coefficient during the switching transition phase, It is the Sigmoid function; The concatenation control input containing the Sigmoid function is 。 2. The method for splicing and controlling a human-machine shared system for Stackelberg game according to claim 1, characterized in that, The human-machine shared control under external disturbances is expressed as a three-person Stackelberg differential game. By solving the parameter-related algebraic Riccati equations, the suboptimal human-machine control strategy and the worst-case disturbance are obtained. The specific steps are as follows: To evaluate system performance and ensure interference attenuation, splicing control inputs were used. Corresponding cost function Defined as in Synchronization error The corresponding weight matrix, For machine control The corresponding weight matrix, For machine control The corresponding weight matrix, Preset Disturbance attenuation index; will have external interference Human-machine shared control is described as a three-person Stackelberg game, where perturbations Acting as a leader to maximize the cost function, machine control and machine control Act as a follower to minimize the cost function; Cost function The corresponding Hamiltonian function is in Representing the cost function Regarding synchronization error The gradient; According to the minimax principle, the saddle point solution... satisfy Solving the above equation yields the suboptimal control input. and maximum disturbance for in Is the positive definite solution to the following algebraic Riccati equation? In the formula ; Suboptimal control input Substituting the concatenated control input containing the Sigmoid function, we can obtain 。